4D seismic, sometimes called time-lapse seismic, is a term that refers to performing an initial, or base, seismic survey of a subsurface region, then performing at least one later, or monitor, survey of the same region, attempting to duplicate the acquisition parameters and conditions as closely as possible. This enables comparison of seismic images of the subsurface over intervals of months or years of time to detect changes due to production of hydrocarbons or injection of fluids. More generally, the term base survey may refer to any survey performed earlier in time than the survey referred to as the monitor survey; i.e., the base survey may refer to an earlier monitor survey. 4D seismic is the only field-wide history match constraint for theoretical predictions of such changes by elaborate software modeling programs called reservoir simulators. Currently, one compares simulator results and 4D seismic data by comparing synthetic seismic data modeled using the simulator to measured 4D seismic difference data; alternatively, acoustic impedance (the product of density and acoustic velocity) modeled using a simulator is compared to inverted impedance, i.e. impedance (as a function of subsurface spatial location) inferred by inversion of seismic data, if inversion has been done. Ideally, the simulator model will be adjusted, or updated, using reservoir properties derived from quantitative 4D, i.e. saturation change and pressure change over time. Data volumes of saturation and pressure change values as a function of subsurface location will reduce the uncertainties in 4D interpretations.
4D seismic is impacted by fluid movement and pressure changes. Production of hydrocarbons resulting in fluid saturation changes in the subsurface will change the acoustic velocity in those regions. Traveling at a different speed (compared to an earlier survey), the reflected seismic wave will arrive at surface detectors sooner or later than in the base survey. This will not only change the strength (amplitude) of the seismic signal, but also shift the apparent depth of reflecting interfaces in resulting seismic images. Moreover, unless pressure is maintained in the reservoir by fluid injection, the reflectors themselves can move downward, which is called subsidence or compaction. However, to quantify the fluid movement and pressure change from the amplitude change of seismic signal, one has to take the rock frame into consideration. Unfortunately, uncertainties in rock parameter estimations (i.e. shale volume vsh and porosity Φ) from seismic can be overwhelming relative to fluid and pressure change signals.
Most current technologies for deriving saturation and pressure change use transformations of variations of 4D AVO attributes (quantities calculated from seismic amplitude vs. source-receiver separation, or offset, data), notably A+B and A−B or IP and IS. (Data for amplitude vs. offset may be subjected to a best linear fit, i.e. y=A+Bx, in which case the parameters A and B are AVO attributes; IP and IS represent acoustic impedance, i.e. the products of density and acoustic wave propagation velocities for the P-wave and S-wave.) Using different forms of approximations of the reflectivity equation (see Aki and Richards, Quantitative Seismology 123-188 (1980)) and a rock physics model, a set of coefficients can be estimated to make a combination of near and far difference amplitudes to infer the saturation and pressure change. See, for example, Landro, “Discrimination between pressure and fluid saturation changes from time-lapse seismic data,” Geophysics 66, 836-844 (2001). This type of formulation works well when physical properties (i.e. vsh and phi) within the reservoir are relatively constant. For a multi-cycle reservoir, side-lobe energy (caused by an input signal such as that from vibroseis for land or airgun for marine being bandwidth limited) may generate apparent difference events that can appear as real reservoir differences. This side-lobe energy complicates interpretation of multi-cycle reservoirs where there is interference between reflectors.
Map-based calibration of production data to 4D seismic data has been seen at SEG conventions (Floricich et al., “An engineering-driven approach for separating pressure and saturation using 4D seismic: application to a Jurassic reservoir in the UK North Sea,” Expanded Abstracts: 75th Annual Meeting of the SEG (2005)). Floricich uses production data (pressure and saturation measurements) at well locations and calibrates each quantity with 4D seismic attributes to derive a map. This method is purely statistical. It does not deal with the vertical distribution of saturation and pressure change.
Time shifts are commonly used for detecting reservoir compaction. The term time shift, or time lag, refers to comparison of time-lapse seismic data and determining the arrival time correction needed to align the subsurface structure in the later seismic data set with that of the earlier data. See, for example, Hudson et al., “Genesis field, Gulf of Mexico, 4-D project status and preliminary lookback,” 75th Annual Meeting of the SEG (2005); and Hatchell et al., “Measuring reservoir compaction using time-lapse timeshifts, Expanded Abstracts,” 75th Annual Meeting of the SEG (2005).
Production will cause the pressure to decrease within the reservoir. If the pressure of the reservoir is not well maintained, compaction of the reservoir will occur, especially for younger rocks. This compaction will most likely be coupled with subsidence of the overburden and overburden dilation. A time-shift data volume can be used to quantify such effects by looking at the time shifts at different time/depth levels.
Most recently, at the 2006 SEG meeting, Rickett et al. (2006) and Janssen et al. (2006) used time shifts to estimate the strain caused by production. (Rickett et al., “Compacting and 4D time strain at the Genesis Field,” and Janssen et al., “Measuring velocity sensitivity to production-induced strain at the Ekofisk Field using time-lapse time-shifts and compaction logs,” both papers in Expanded Abstracts: 76th Annual Meeting of the SEG (2006)) Jansen showed that taking the first derivative of the time shifts enables interpreters to interpret time shifts in a manner similar to interpreting 4D difference volumes. Veire et al. disclose a stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data within a Bayesian framework. (“Stochastic Inversion of Pressure and Saturation Changes From Time-Lapse AVO Data,” Geophysics 71, C81-C92 (2006))